Search engines provide an interface to a group of items that enables users to specify criteria about an item of interest and to have the engine find the matching items. The criteria are referred to as a search query. In the case of text search engines, the search query is typically expressed as a set of words that identify the desired concept that one or more documents may contain. Text search engines are described, for example, in a dissertation by Voorhees entitled “Natural Language Processing and Information Retrieval,” (National Institute of Standards and Technology, 2000).
The World Wide Web (web) provides a large collection of interlinked information in various sources including documents, images, and media content relating to nearly every subject. Early web search engines provided basic algorithmic search, followed by paid search business models to fuel innovation. The third era of web searching is social search, harnessing human decision making to provide the most important and subjective feature in search results, relevance. Inferring each user's intent from an average of only 2.1 search terms remains at the core of the relevance challenge, as described in “The Impending Social Search Inflection Point, 2007,” an article available at the Search Engine Land web site (searchengineland.com). The article notes that after “on-the-page” and “off-the-page” criteria, web connectivity and link authority, relevance is now increasingly augmented by implicit and explicit user behaviors, social networks and communities.
US Patent Application Publication 2006/0235873, whose disclosure is incorporated herein by reference, describes a method for filtering internet content responsively to a search query message based upon the user's web filters as well as the web filters of other selected users. The web experiences of a number of individuals that the user selects as reflective of the user's own preferences are leveraged to formulate an algorithm reflecting the user's preferences. The collective experience of a whole social network rapidly populates a filter to build a greater likelihood of locating information that will satisfy a user's needs according to his or her preferences.